{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.6802720745909845e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularization loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "0.01896541960606335\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.10061918  1.14472371  0.90159072]\n",
      " [ 0.50249434  0.90085595 -0.68372786]\n",
      " [-0.12289023 -0.93576943 -0.26788808]]\n",
      "\n",
      "[[ 0.50249434  0.90085595 -0.68372786]\n",
      " [-0.12289023 -0.93576943 -0.26788808]\n",
      " [-1.10061918  1.14472371  0.90159072]]\n",
      "\n",
      "[ 1.14472371 -0.68372786 -0.12289023]\n",
      "0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "a=np.random.randn(3,3)\n",
    "print(a)\n",
    "b=np.array([1,2,0])\n",
    "c=[1,3,0]\n",
    "print()\n",
    "print(a[range(3)][b])\n",
    "print()\n",
    "print(a[range(3),b])\n",
    "print((b==c).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W2 max relative error: 3.440708e-09\n",
      "b2 max relative error: 4.447625e-11\n",
      "W1 max relative error: 3.561318e-09\n",
      "b1 max relative error: 2.738421e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "final_training_loss"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 16.24345364  -6.11756414  -5.28171752 -10.72968622]\n",
      " [  8.65407629 -23.01538697  17.44811764  -7.61206901]\n",
      " [  3.19039096  -2.49370375  14.62107937 -20.60140709]\n",
      " [ -3.22417204  -3.84054355  11.33769442 -10.99891267]\n",
      " [ -1.72428208  -8.77858418   0.42213747   5.82815214]]\n",
      "iteration 0 / 100: loss 1.241992\n",
      "Final training loss:  0.01714364581545526\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X)\n",
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=True)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302762\n",
      "iteration 100 / 1000: loss 2.302358\n",
      "iteration 200 / 1000: loss 2.297405\n",
      "iteration 300 / 1000: loss 2.258897\n",
      "iteration 400 / 1000: loss 2.202976\n",
      "iteration 500 / 1000: loss 2.116818\n",
      "iteration 600 / 1000: loss 2.049801\n",
      "iteration 700 / 1000: loss 1.985725\n",
      "iteration 800 / 1000: loss 2.003727\n",
      "iteration 900 / 1000: loss 1.948067\n",
      "Validation accuracy:  0.287\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "            reg=0.25, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Explain your hyperparameter tuning process below.**\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-03 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidder_size:5.000000e+01\n",
      "iteration 0 / 1000: loss 2.303351\n",
      "iteration 100 / 1000: loss 1.945000\n",
      "iteration 200 / 1000: loss 1.820054\n",
      "iteration 300 / 1000: loss 1.836323\n",
      "iteration 400 / 1000: loss 1.771951\n",
      "iteration 500 / 1000: loss 1.657281\n",
      "iteration 600 / 1000: loss 1.612927\n",
      "iteration 700 / 1000: loss 1.679998\n",
      "iteration 800 / 1000: loss 1.686324\n",
      "iteration 900 / 1000: loss 1.587177\n",
      "iteration 999 / 1000: loss 1.466766\n",
      "lr 1.000000e-03 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidder_size:1.000000e+02\n",
      "iteration 0 / 1000: loss 2.304154\n",
      "iteration 100 / 1000: loss 1.980725\n",
      "iteration 200 / 1000: loss 1.858321\n",
      "iteration 300 / 1000: loss 1.711422\n",
      "iteration 400 / 1000: loss 1.800754\n",
      "iteration 500 / 1000: loss 1.535711\n",
      "iteration 600 / 1000: loss 1.579644\n",
      "iteration 700 / 1000: loss 1.646542\n",
      "iteration 800 / 1000: loss 1.520497\n",
      "iteration 900 / 1000: loss 1.649496\n",
      "iteration 999 / 1000: loss 1.595624\n",
      "lr 1.000000e-03 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidder_size:1.500000e+02\n",
      "iteration 0 / 1000: loss 2.304892\n",
      "iteration 100 / 1000: loss 2.042844\n",
      "iteration 200 / 1000: loss 1.818616\n",
      "iteration 300 / 1000: loss 1.801032\n",
      "iteration 400 / 1000: loss 1.700986\n",
      "iteration 500 / 1000: loss 1.678688\n",
      "iteration 600 / 1000: loss 1.635282\n",
      "iteration 700 / 1000: loss 1.608126\n",
      "iteration 800 / 1000: loss 1.581807\n",
      "iteration 900 / 1000: loss 1.544577\n",
      "iteration 999 / 1000: loss 1.475409\n",
      "lr 1.000000e-03 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidder_size:2.000000e+02\n",
      "iteration 0 / 1000: loss 2.305728\n",
      "iteration 100 / 1000: loss 1.974097\n",
      "iteration 200 / 1000: loss 1.814045\n",
      "iteration 300 / 1000: loss 1.845098\n",
      "iteration 400 / 1000: loss 1.793389\n",
      "iteration 500 / 1000: loss 1.603517\n",
      "iteration 600 / 1000: loss 1.714750\n",
      "iteration 700 / 1000: loss 1.574995\n",
      "iteration 800 / 1000: loss 1.625504\n",
      "iteration 900 / 1000: loss 1.588054\n",
      "iteration 999 / 1000: loss 1.607990\n",
      "lr 1.000000e-03 ld 8.000000e-01  reg 1.000000e+00 bs 3.000000e+02 hidder_size:5.000000e+01\n",
      "iteration 0 / 1000: loss 2.303337\n",
      "iteration 100 / 1000: loss 2.009408\n",
      "iteration 200 / 1000: loss 1.769529\n",
      "iteration 300 / 1000: loss 1.661950\n",
      "iteration 400 / 1000: loss 1.604536\n",
      "iteration 500 / 1000: loss 1.575339\n",
      "iteration 600 / 1000: loss 1.622587\n",
      "iteration 700 / 1000: loss 1.742443\n",
      "iteration 800 / 1000: loss 1.695741\n",
      "iteration 900 / 1000: loss 1.537282\n",
      "iteration 999 / 1000: loss 1.556128\n",
      "lr 1.000000e-03 ld 8.000000e-01  reg 1.000000e+00 bs 3.000000e+02 hidder_size:1.000000e+02\n",
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    },
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     ]
    }
   ],
   "source": [
    "best_net = None # store the best model into this \n",
    "best_accuracy=-1\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "batchSizes=[200,300,400]\n",
    "learningRates=[1e-3,5e-4,1e-4,5e-4]\n",
    "learningDecays=[0.8,0.85,0.9,0.95]\n",
    "regs=[1,2.5,3]\n",
    "hiddenSizes=[50,100,150,200]\n",
    "results={}\n",
    "# stats = net.train(X_train, y_train, X_val, y_val,\n",
    "#             num_iters=1000, batch_size=200,\n",
    "#             learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "#             reg=0.25, verbose=True)\n",
    "for lr in learningRates:\n",
    "    for ld in learningDecays:\n",
    "        for rg in regs:\n",
    "            for bs in batchSizes:\n",
    "                for hidden_size in hiddenSizes:\n",
    "                    print('lr %e ld %e  reg %e bs %e hidder_size:%e'%(lr,ld,rg,bs,hidden_size))\n",
    "                    net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "                    net.train(X_train,y_train,X_val,y_val,learning_rate=lr,learning_rate_decay=ld,\n",
    "                          num_iters=1000,batch_size=bs,reg=rg,verbose=True)\n",
    "                    YValPred=net.predict(X_val)\n",
    "                    Val_accuracy=(YValPred==y_val).mean()\n",
    "                    YTrainPred=net.predict(X_train)\n",
    "                    Train_accuracy=(YTrainPred==y_train).mean()\n",
    "                    results[(lr,ld,rg,bs,hidden_size)]=(Train_accuracy,Val_accuracy)\n",
    "                    if Val_accuracy>best_accuracy:\n",
    "                        best_accuracy=Val_accuracy\n",
    "                        best_net=net\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.236551 val accuracy: 0.251000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.248592 val accuracy: 0.258000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.251000 val accuracy: 0.257000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.252837 val accuracy: 0.263000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.264306 val accuracy: 0.264000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.278980 val accuracy: 0.282000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.286306 val accuracy: 0.278000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.295184 val accuracy: 0.291000\n",
      "lr 1.000000e-04 ld 8.000000e-01  reg 1.000000e+00 bs 4.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.238898 val accuracy: 0.250000\n",
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      "lr 1.000000e-03 ld 9.000000e-01  reg 2.500000e+00 bs 3.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.471490 val accuracy: 0.462000\n",
      "lr 1.000000e-03 ld 9.000000e-01  reg 2.500000e+00 bs 3.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.474918 val accuracy: 0.460000\n",
      "lr 1.000000e-03 ld 9.000000e-01  reg 2.500000e+00 bs 3.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.473204 val accuracy: 0.470000\n",
      "lr 1.000000e-03 ld 9.000000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.465184 val accuracy: 0.468000\n",
      "lr 1.000000e-03 ld 9.000000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.473878 val accuracy: 0.463000\n",
      "lr 1.000000e-03 ld 9.000000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.475878 val accuracy: 0.462000\n",
      "lr 1.000000e-03 ld 9.000000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.473980 val accuracy: 0.466000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.479592 val accuracy: 0.447000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.490020 val accuracy: 0.467000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.493000 val accuracy: 0.478000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 2.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.495061 val accuracy: 0.470000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.491347 val accuracy: 0.477000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.492878 val accuracy: 0.474000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.502163 val accuracy: 0.477000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 3.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.503408 val accuracy: 0.466000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 4.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.486327 val accuracy: 0.465000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 4.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.500020 val accuracy: 0.477000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 4.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.506265 val accuracy: 0.488000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 1.000000e+00 bs 4.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.509551 val accuracy: 0.495000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 2.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.460061 val accuracy: 0.451000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 2.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.469878 val accuracy: 0.465000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 2.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.472918 val accuracy: 0.471000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 2.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.466959 val accuracy: 0.474000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 3.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.464959 val accuracy: 0.454000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 3.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.475367 val accuracy: 0.450000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 3.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.473571 val accuracy: 0.469000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 3.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.477612 val accuracy: 0.461000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:5.000000e+01 train accuracy: 0.468163 val accuracy: 0.462000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:1.000000e+02 train accuracy: 0.476020 val accuracy: 0.460000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:1.500000e+02 train accuracy: 0.477878 val accuracy: 0.466000\n",
      "lr 1.000000e-03 ld 9.500000e-01  reg 2.500000e+00 bs 4.000000e+02 hidden_Size:2.000000e+02 train accuracy: 0.481918 val accuracy: 0.475000\n",
      "best_parameter:lr:0.001000,ld:0.950000,reg:1.000000,bs:400.000000,hs:200.000000\n",
      "best_accuracy: 0.495000\n"
     ]
    }
   ],
   "source": [
    "for lr,ld,reg,bs,hs in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr,ld, reg,bs,hs)]\n",
    "    print('lr %e ld %e  reg %e bs %e hidden_Size:%e train accuracy: %f val accuracy: %f' % (\n",
    "                lr,ld, reg,bs, hs,train_accuracy, val_accuracy))\n",
    "    if val_accuracy==best_accuracy:\n",
    "        best_parameter=(lr,ld,reg,bs,hs)\n",
    "print('best_parameter:lr:%f,ld:%f,reg:%f,bs:%f,hs:%f'%(best_parameter[0],best_parameter[1],best_parameter[2],best_parameter[3],best_parameter[4]))\n",
    "print('best_accuracy: %f' % (best_accuracy ))\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "val_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy:  0.495\n"
     ]
    }
   ],
   "source": [
    "# Print your validation accuracy: this should be above 48%\n",
    "val_acc = (best_net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the weights of the best network\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "test_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.497\n"
     ]
    }
   ],
   "source": [
    "# Print your test accuracy: this should be above 48%\n",
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question**\n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(results,open('results.pkl','wb'))\n",
    "pickle.dump(best_accuracy,open('best_accuracy.pkl','wb'))\n",
    "pickle.dump(best_net.params,open('best_net.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'W1': array([[-2.88575632e-04, -5.28338981e-05,  5.74100182e-04, ...,\n",
       "          5.77135948e-04,  2.73424297e-04, -2.88504225e-04],\n",
       "        [-9.88821892e-04, -1.92629781e-04,  1.25913744e-04, ...,\n",
       "          5.45552233e-04,  2.39077791e-04, -1.48583915e-04],\n",
       "        [-4.40924985e-04, -2.77579015e-04,  1.64571448e-04, ...,\n",
       "          4.04434452e-04, -5.04838997e-05, -2.41969199e-04],\n",
       "        ...,\n",
       "        [-2.75689907e-04, -9.00368588e-04, -5.17558139e-04, ...,\n",
       "          3.33719498e-04,  2.58463736e-04,  8.92134799e-04],\n",
       "        [ 2.34922484e-05,  6.12560115e-05, -4.51691944e-04, ...,\n",
       "         -1.02491976e-04, -1.65336492e-06,  1.11207718e-03],\n",
       "        [-4.93665413e-04,  6.31068658e-04,  3.22413872e-04, ...,\n",
       "         -6.20641346e-04, -2.15338597e-04,  7.26471854e-05]]),\n",
       " 'b1': array([ 1.03965253e-05,  1.26074147e-05, -2.21693037e-06, -5.93029087e-06,\n",
       "         1.69651562e-05, -1.17781796e-05,  1.09010327e-05,  1.16139053e-04,\n",
       "         1.22109487e-05, -2.54863259e-06, -2.54028209e-05,  1.72482143e-04,\n",
       "         4.11684487e-05, -3.48991618e-05, -1.32122893e-05, -1.64348844e-06,\n",
       "         6.02261720e-05,  1.57562350e-05,  1.03907476e-05,  1.20373523e-05,\n",
       "         5.01993155e-05,  7.74272524e-05, -7.46929178e-06,  2.38061554e-05,\n",
       "         4.20415476e-05,  5.30002464e-05,  4.11858741e-05, -4.67073284e-05,\n",
       "         1.99211878e-05,  1.91027320e-05, -2.31747767e-06, -3.10006161e-06,\n",
       "         1.49504831e-04,  1.58109482e-05,  3.89991960e-05,  1.62500526e-05,\n",
       "         4.76893377e-05,  1.19428161e-04, -1.39134796e-05,  1.02057984e-04,\n",
       "         3.56875134e-05,  6.92170840e-06,  3.36597310e-05,  1.68384111e-05,\n",
       "        -1.09631716e-05,  1.44574090e-04,  1.17985276e-06, -6.48997524e-05,\n",
       "         7.80466832e-05,  1.02239591e-05,  1.34146273e-04,  7.09701629e-06,\n",
       "         1.34989550e-04, -2.17740546e-06,  1.70584091e-06, -2.20661921e-06,\n",
       "         4.31207972e-06,  5.78809140e-07,  3.33001194e-06,  8.96001760e-06,\n",
       "        -2.39308111e-06, -1.75992738e-05,  1.20698993e-05, -2.45138513e-06,\n",
       "         8.76768376e-06,  7.98545215e-05,  1.23943474e-05,  2.87631395e-05,\n",
       "         8.54312996e-06,  4.67545918e-05,  1.77118176e-05,  1.22792097e-04,\n",
       "        -3.20961655e-05,  5.27885059e-05,  7.19159327e-05,  3.13169009e-05,\n",
       "        -7.20183947e-06,  6.75467779e-07,  9.47438089e-05,  5.01621356e-05,\n",
       "         2.80541947e-05,  3.01099068e-05,  1.36066925e-05,  1.54333149e-05,\n",
       "         6.76999559e-05,  2.20972317e-05,  2.02322083e-05, -1.35128092e-05,\n",
       "        -7.31259313e-05, -2.23323760e-05,  1.09549395e-04,  4.50969255e-05,\n",
       "         6.24900326e-05,  1.37114856e-05, -8.02869473e-06,  5.28998070e-06,\n",
       "         2.35848053e-05, -2.99883314e-06,  1.89909465e-05, -1.25777951e-05,\n",
       "        -8.27824591e-06, -4.84558406e-06,  2.48678124e-07,  2.39416069e-05,\n",
       "         4.02385104e-05,  5.78725177e-06,  2.67528376e-05, -2.18034207e-05,\n",
       "         7.59385591e-05, -2.83057140e-06,  9.94652056e-06, -1.72101945e-05,\n",
       "         4.61879602e-06, -1.34811906e-05,  3.86300207e-06,  6.13703764e-05,\n",
       "        -1.28655297e-05, -2.09271217e-05,  1.54670888e-05, -1.79652463e-05,\n",
       "         1.76491441e-05, -2.43776058e-05,  8.86904868e-05,  2.30956117e-05,\n",
       "         8.64551721e-06, -3.04239348e-05,  4.52358273e-05,  2.40891741e-05,\n",
       "         3.07346773e-05,  1.16475692e-06,  1.21104806e-05, -2.64042481e-05,\n",
       "        -1.03051820e-05,  9.28565867e-05,  1.26380243e-05,  1.60976909e-05,\n",
       "        -8.78551810e-06,  5.39935471e-06, -1.01948938e-05,  2.20322851e-05,\n",
       "         7.77629435e-05,  1.89528291e-05, -4.46411943e-06,  5.65901421e-05,\n",
       "        -9.94078729e-06,  1.66705095e-05,  5.29905816e-05,  1.56457143e-05,\n",
       "        -1.14714937e-05,  4.73741901e-07, -1.86282520e-06,  1.55167257e-05,\n",
       "         1.17725634e-05,  1.13987386e-05,  1.12425627e-05,  1.86569228e-06,\n",
       "         1.09286023e-05,  1.50467299e-05,  9.60983529e-05,  1.25332885e-05,\n",
       "         2.25453025e-05,  1.44019818e-05, -2.26299132e-05,  2.21954086e-05,\n",
       "         2.47039532e-05,  2.41777147e-05, -1.90813562e-05, -3.30210455e-05,\n",
       "        -3.65231984e-06,  4.93970371e-05,  4.69176506e-05,  7.14223110e-05,\n",
       "         4.85288088e-05,  2.96271904e-06,  9.22744656e-06,  9.21984454e-06,\n",
       "         8.81083984e-05,  1.31369873e-04,  1.25316208e-05,  2.49727150e-05,\n",
       "        -3.78202412e-05,  6.32557409e-05,  2.82293109e-05,  6.20177366e-07,\n",
       "         1.39700941e-05,  9.45018148e-05,  1.85557552e-05,  5.80987060e-05,\n",
       "         1.40285955e-04,  9.35741481e-06, -4.25485755e-05,  2.42788456e-05,\n",
       "        -2.20935171e-05,  1.32329093e-04,  2.07842466e-05,  8.43100880e-05,\n",
       "        -2.83401979e-05, -4.63281577e-05, -7.32908089e-06,  5.85689132e-05]),\n",
       " 'W2': array([[-0.00576576, -0.00059578, -0.01250664, ...,  0.00134784,\n",
       "          0.00653115, -0.00755918],\n",
       "        [-0.00727906, -0.00228401, -0.00504648, ...,  0.00257288,\n",
       "          0.01289577, -0.00439759],\n",
       "        [-0.00329245, -0.00483387, -0.00503942, ..., -0.00346124,\n",
       "          0.00581428, -0.00472565],\n",
       "        ...,\n",
       "        [ 0.00236069,  0.00920477,  0.00425653, ...,  0.01355148,\n",
       "         -0.00975676,  0.00039624],\n",
       "        [ 0.00192313,  0.00530393, -0.00214661, ...,  0.00761045,\n",
       "         -0.00046137, -0.00346289],\n",
       "        [-0.00758186, -0.00496322, -0.00198153, ...,  0.01063119,\n",
       "         -0.00020345, -0.0020347 ]]),\n",
       " 'b2': array([-0.00285162, -0.00431025,  0.00378115, -0.00144584,  0.00532216,\n",
       "        -0.00064568,  0.00338617,  0.00054294, -0.00186666, -0.00191236])}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_net.params"
   ]
  }
 ],
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